
"""simple docstring"""


import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.12.2'''):
		raise Exception('''requires fairseq >= 0.12.2''')
if version.parse(fairseq.__version__) > version.parse('''2'''):
		raise Exception('''requires fairseq < v2''')

logging.set_verbosity_info()
_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					'''Hello, World!'''
_lowercase				 =					'''en_XX'''






def    _snake_case					(		snake_case__ :  str						,   snake_case__ :  str						,   snake_case__ :  bool  ):
	A     = Path('data_bin'  )
	A     = FairseqXmodModel.from_pretrained(
	    model_name_or_path=str(Path(snake_case__  ).parent  )						,   checkpoint_file=Path(snake_case__  ).name						,   _name='xmod_base'						,   arch='xmod_base'						,   task='multilingual_masked_lm'						,   data_name_or_path=str(snake_case__  )						,   bpe='sentencepiece'						,   sentencepiece_model=str(Path(snake_case__  ).parent / 'sentencepiece.bpe.model'  )						,   src_dict=str(data_dir / 'dict.txt'  )						,   )
	xmod.eval()  # disable dropout
	print(snake_case__  )

	A     = xmod.model.encoder.sentence_encoder
	A     = XmodConfig(
	    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings						,   hidden_size=xmod.cfg.model.encoder_embed_dim						,   num_hidden_layers=xmod.cfg.model.encoder_layers						,   num_attention_heads=xmod.cfg.model.encoder_attention_heads						,   intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim						,   max_position_embeddings=514						,   type_vocab_size=1						,   layer_norm_eps=1e-5						,   pre_norm=xmod.cfg.model.encoder_normalize_before						,   adapter_reduction_factor=getattr(xmod.cfg.model						,   'bottleneck'						,   2  )						,   adapter_layer_norm=xmod.cfg.model.adapter_layer_norm						,   adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm						,   ln_before_adapter=xmod.cfg.model.ln_before_adapter						,   languages=xmod.cfg.model.languages						,   )
	if classification_head:
		A     = xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

	print('Our X-MOD config:'						,   snake_case__  )

	A     = XmodForSequenceClassification(snake_case__  ) if classification_head else XmodForMaskedLM(snake_case__  )
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	A     = xmod_sent_encoder.embed_tokens.weight
	A     = xmod_sent_encoder.embed_positions.weight
	A     = torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c xmod doesn't use them.

	A     = xmod_sent_encoder.layernorm_embedding.weight
	A     = xmod_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers  ):
		# Encoder: start of layer
		A     = model.roberta.encoder.layer[i]
		A     = xmod_sent_encoder.layers[i]

		# self attention
		A     = layer.attention.self
		if not (
		    xmod_layer.self_attn.k_proj.weight.data.shape
		    == xmod_layer.self_attn.q_proj.weight.data.shape
		    == xmod_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)  )
		):
			raise AssertionError('Dimensions of self-attention weights do not match.'  )

		A     = xmod_layer.self_attn.q_proj.weight
		A     = xmod_layer.self_attn.q_proj.bias
		A     = xmod_layer.self_attn.k_proj.weight
		A     = xmod_layer.self_attn.k_proj.bias
		A     = xmod_layer.self_attn.v_proj.weight
		A     = xmod_layer.self_attn.v_proj.bias

		# self-attention output
		A     = layer.attention.output
		if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
			raise AssertionError('Dimensions of self-attention output weights do not match.'  )
		A     = xmod_layer.self_attn.out_proj.weight
		A     = xmod_layer.self_attn.out_proj.bias
		A     = xmod_layer.self_attn_layer_norm.weight
		A     = xmod_layer.self_attn_layer_norm.bias

		# intermediate
		A     = layer.intermediate
		if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('Dimensions of intermediate weights do not match.'  )
		A     = xmod_layer.fca.weight
		A     = xmod_layer.fca.bias

		# output
		A     = layer.output
		if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('Dimensions of feed-forward weights do not match.'  )
		A     = xmod_layer.fca.weight
		A     = xmod_layer.fca.bias
		A     = xmod_layer.final_layer_norm.weight
		A     = xmod_layer.final_layer_norm.bias
		if bert_output.adapter_layer_norm is not None:
			A     = xmod_layer.adapter_layer_norm.weight
			A     = xmod_layer.adapter_layer_norm.bias

		if sorted(bert_output.adapter_modules.keys()  ) != sorted(xmod_layer.adapter_modules.keys()  ):
			raise AssertionError('Lists of language adapters do not match.'  )
		for lang_code, adapter in xmod_layer.adapter_modules.items():
			A     = bert_output.adapter_modules[lang_code]
			A     = xmod_layer.adapter_modules[lang_code]
			A     = from_adapter.fca.weight
			A     = from_adapter.fca.bias
			A     = from_adapter.fca.weight
			A     = from_adapter.fca.bias

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		A     = xmod_sent_encoder.layer_norm.weight
		A     = xmod_sent_encoder.layer_norm.bias

	if classification_head:
		A     = xmod.model.classification_heads['mnli'].dense.weight
		A     = xmod.model.classification_heads['mnli'].dense.bias
		A     = xmod.model.classification_heads['mnli'].out_proj.weight
		A     = xmod.model.classification_heads['mnli'].out_proj.bias
	else:
		# LM Head
		A     = xmod.model.encoder.lm_head.dense.weight
		A     = xmod.model.encoder.lm_head.dense.bias
		A     = xmod.model.encoder.lm_head.layer_norm.weight
		A     = xmod.model.encoder.lm_head.layer_norm.bias
		A     = xmod.model.encoder.lm_head.weight
		A     = xmod.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	A     = xmod.encode(snake_case__  ).unsqueeze(0  )  # batch of size 1
	model.roberta.set_default_language(snake_case__  )

	A     = model(snake_case__  )[0]
	if classification_head:
		A     = xmod.model.classification_heads['mnli'](xmod.extract_features(snake_case__  )  )
	else:
		A     = xmod.model(snake_case__						,   lang_id=[SAMPLE_LANGUAGE]  )[0]
	print(our_output.shape						,   their_output.shape  )
	A     = torch.max(torch.abs(our_output - their_output  )  ).item()
	print(F'max_absolute_diff = {max_absolute_diff}'  )  # ~ 1e-7
	A     = torch.allclose(snake_case__						,   snake_case__						,   atol=1e-3  )
	print('Do both models output the same tensors?'						,   '🔥' if success else '💩'  )
	if not success:
		raise Exception('Something went wRoNg'  )

	Path(snake_case__  ).mkdir(parents=snake_case__						,   exist_ok=snake_case__  )
	print(F'Saving model to {pytorch_dump_folder_path}'  )
	model.save_pretrained(snake_case__  )


if __name__ == "__main__":
		_lowercase				 =					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--xmod_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
		)
		_lowercase				 =					parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)




'''simple docstring'''






import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase:   Tuple					=		logging.get_logger(__name__)

lowerCAmelCase:   Dict					=		'▁'

lowerCAmelCase:   Dict					=		{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

lowerCAmelCase:   int					=		{
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

lowerCAmelCase:   Any					=		{
    'facebook/s2t-small-librispeech-asr': 1_0_2_4,
}

lowerCAmelCase:   Optional[int]					=		['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

lowerCAmelCase:   List[Any]					=		{'mustc': MUSTC_LANGS}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       VOCAB_FILES_NAMES
    lowercase__					=       PRETRAINED_VOCAB_FILES_MAP
    lowercase__					=       MAX_MODEL_INPUT_SIZES
    lowercase__					=       ["""input_ids""", """attention_mask"""]

    lowercase__					=       []







    def __init__( self					:    int		,     __snake_case					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Optional[Any]="<s>"		,     __snake_case					:    Optional[int]="</s>"		,     __snake_case					:    Union[str, Any]="<pad>"		,     __snake_case					:    str="<unk>"		,     __snake_case					:    Dict=False		,     __snake_case					:    int=False		,     __snake_case					:    str=None		,     __snake_case					:    Optional[int]=None		,     __snake_case					:    Optional[Dict[str, Any]] = None		,     **__snake_case					:    Union[str, Any]		,     ):
      a		:   Union[str, Any]	  =	{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=__snake_case		,     eos_token=__snake_case		,     unk_token=__snake_case		,     pad_token=__snake_case		,     do_upper_case=__snake_case		,     do_lower_case=__snake_case		,     tgt_lang=__snake_case		,     lang_codes=__snake_case		,     sp_model_kwargs=self.sp_model_kwargs		,     **__snake_case		,     )
      a		:   Tuple	  =	do_upper_case
      a		:   Optional[Any]	  =	do_lower_case

      a		:   List[str]	  =	load_json(__snake_case      )
      a		:   Dict	  =	{v: k for k, v in self.encoder.items()}
      a		:   int	  =	spm_file
      a		:   Tuple	  =	load_spm(__snake_case		,     self.sp_model_kwargs      )

      if lang_codes is not None:
        a		:   Any	  =	lang_codes
        a		:   str	  =	LANGUAGES[lang_codes]
        a		:   Tuple	  =	[F"""<lang:{lang}>""" for lang in self.langs]
        a		:   str	  =	{lang: self.sp_model.PieceToId(F"""<lang:{lang}>"""      ) for lang in self.langs}

        a		:   Optional[Any]	  =	self.lang_tokens
        a		:   Union[str, Any]	  =	tgt_lang if tgt_lang is not None else self.langs[0]

        self.set_tgt_lang_special_tokens(self._tgt_lang      )
      else:
        a		:   List[str]	  =	{}







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      return len(self.encoder      )







    @property
    def      lowercase_			( self					:    int      ):
      return self._tgt_lang







    @tgt_lang.setter
    def      lowercase_			( self					:    int		,     __snake_case					:    Optional[int]      ):
      a		:   Union[str, Any]	  =	new_tgt_lang
      self.set_tgt_lang_special_tokens(__snake_case      )







    def      lowercase_			( self					:    str		,     __snake_case					:    str      ):
      a		:   int	  =	self.lang_code_to_id[tgt_lang]
      a		:   int	  =	[lang_code_id]







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    str      ):
      return self.sp_model.encode(__snake_case		,     out_type=__snake_case      )







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    List[Any]      ):
      return self.encoder.get(__snake_case		,     self.encoder[self.unk_token]      )







    def      lowercase_			( self					:    List[Any]		,     __snake_case					:    int      ):
      return self.decoder.get(__snake_case		,     self.unk_token      )







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    List[str]      ):
      a		:   List[Any]	  =	[]
      a		:   List[Any]	  =	''
      for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
          a		:   Union[str, Any]	  =	self.sp_model.decode(__snake_case      )
          out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
          a		:   Optional[int]	  =	[]
        else:
          current_sub_tokens.append(__snake_case      )
      a		:   Tuple	  =	self.sp_model.decode(__snake_case      )
      out_string += decoded.upper() if self.do_upper_case else decoded
      return out_string.strip()







    def      lowercase_			( self					:    int		,     __snake_case					:    List[Any]		,     __snake_case					:    List[str]=None      ):
      if token_ids_a is None:
        return self.prefix_tokens + token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    List[int]		,     __snake_case					:    Optional[List[int]] = None		,     __snake_case					:    bool = False      ):

      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__snake_case		,     token_ids_a=__snake_case		,     already_has_special_tokens=__snake_case      )

      a		:   Optional[int]	  =	[1] * len(self.prefix_tokens      )
      a		:   Any	  =	[1]
      if token_ids_a is None:
        return prefix_ones + ([0] * len(__snake_case      )) + suffix_ones
      return prefix_ones + ([0] * len(__snake_case      )) + ([0] * len(__snake_case      )) + suffix_ones







    def      lowercase_			( self					:    Union[str, Any]      ):
      a		:   Union[str, Any]	  =	self.encoder.copy()
      vocab.update(self.added_tokens_encoder      )
      return vocab







    def __getstate__( self					:    Any      ):
      a		:   List[str]	  =	self.__dict__.copy()
      a		:   Union[str, Any]	  =	None
      return state







    def __setstate__( self					:    str		,     __snake_case					:    Dict      ):
      a		:   Dict	  =	d

      # for backward compatibility
      if not hasattr(self		,     'sp_model_kwargs'      ):
        a		:   int	  =	{}

      a		:   Any	  =	load_spm(self.spm_file		,     self.sp_model_kwargs      )







    def      lowercase_			( self					:    str		,     __snake_case					:    str		,     __snake_case					:    Optional[str] = None      ):
      a		:   Union[str, Any]	  =	Path(__snake_case      )
      assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
      a		:   Any	  =	save_dir / (
          (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
      )
      a		:   List[Any]	  =	save_dir / (
          (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
      )

      save_json(self.encoder		,     __snake_case      )

      if os.path.abspath(self.spm_file      ) != os.path.abspath(__snake_case      ) and os.path.isfile(self.spm_file      ):
        copyfile(self.spm_file		,     __snake_case      )
      elif not os.path.isfile(self.spm_file      ):
        with open(__snake_case		,     'wb'      ) as fi:
          a		:   Tuple	  =	self.sp_model.serialized_model_proto()
          fi.write(__snake_case      )

      return (str(__snake_case      ), str(__snake_case      ))
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   List[Any]	  =	sentencepiece.SentencePieceProcessor(**_A							)
  spm.Load(str(_A							)							)
  return spm
def  lowerCamelCase__			(		_A							):
  with open(_A					,     'r'							) as f:
    return json.load(_A							)

def  lowerCamelCase__			(		_A					,     _A							):
  with open(_A					,     'w'							) as f:
    json.dump(_A					,     _A					,     indent=2							)